Lesson 0 - LinRegression.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Linear Regression\n",
    "\n",
    "Machine learning is about using data to infer patterns.\n",
    "\n",
    "Linear regression is the basic unit of Deep Learning. In fact personally I prefer to think of it as stacking linear regression modules.\n",
    "\n",
    "Deep Learning has proven itself over and over again to be far superior to traditional machine learning techiques. It's strength comes from the fact that it can be scalable to large amounts of data. \n",
    "\n",
    "Some of the most impressive applications are in voice recognition, self driving cars and in chatbots."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
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GDEWMsSE7xMTxUS9VzuRF3M5s2zIt9na2qZ/6qnv5fF4hhkodpcThyzjvkYXq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\ntJwWnd4hlq2V0faTgtO7xADpkDmbLVs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      "text/html": [
       "\n",
       "        <iframe\n",
       "            width=\"400\"\n",
       "            height=\"300\"\n",
       "            src=\"https://www.youtube.com/embed/jpmGxOB2YPo\"\n",
       "            frameborder=\"0\"\n",
       "            allowfullscreen\n",
       "        ></iframe>\n",
       "        "
      ],
      "text/plain": [
       "<IPython.lib.display.YouTubeVideo at 0x7fba2c561320>"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from IPython.display import YouTubeVideo\n",
    "YouTubeVideo(\"jpmGxOB2YPo\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Consider the line $y=2x+3$. See below for a light introduction to plotting and numpy arrays. Numpy arrays are really important to understand. If you have experience with Matlab once you take out the `np.` part it should be familiar to the relevant Matlab function."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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AIjNb7e6fnMwDhcJjBkBSUpJHqD6RqLR9fz73vZPK4lU59OvYlMeuGkzPtmreJpERkQBw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\nTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fba0c569860>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "x = np.linspace(-5,5,num=100)\n",
    "y = 2*x+3\n",
    "\n",
    "plt.plot(x,y)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This is how you define a function in Python. Remember to return the value."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def f(w,b):\n",
    "    \"\"\"\n",
    "    We will limit ourselves to a linear function\n",
    "    \"\"\"\n",
    "    return w*x+b\n",
    "\n",
    "def loss_function(w,b):\n",
    "    \"\"\"\n",
    "    Loss function (squared error)\n",
    "    Note that w and b are the ONLY variables\n",
    "    \"\"\"\n",
    "    e = y - f(w,b)\n",
    "    return np.mean(np.square(e))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Find the function\n",
    "\n",
    "Given the observation $x,y$ the goal is to find the best parameters, $w,b$ that fits the data. Ignore the fact that you can solve this as an equation. Most of the time the data isn't as linear or noiseless as what is being presented here.\n",
    "\n",
    "The most naiive way of finding $w$ and $b$ is to do a grid search over $w$ and $b$ for the smallest loss function value. In this case the loss function is: $$\\frac{1}{N}\\sum_{i=1}^N \\xi_i^2$$ where, $\\xi_i = y_i - f(x_i|w,b)$ and $f(x_i|w,b)=wx_i+b$.\n",
    "\n",
    "Another way of stating the above is that we need to find the best fitting function $f(x_i|w,b)$ such that we minimising the __loss function__ by varying $w$ and $b$. An illustration is shown below:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ -7.        ,  -6.7979798 ,  -6.5959596 ,  -6.39393939,\n",
       "        -6.19191919,  -5.98989899,  -5.78787879,  -5.58585859,\n",
       "        -5.38383838,  -5.18181818,  -4.97979798,  -4.77777778,\n",
       "        -4.57575758,  -4.37373737,  -4.17171717,  -3.96969697,\n",
       "        -3.76767677,  -3.56565657,  -3.36363636,  -3.16161616,\n",
       "        -2.95959596,  -2.75757576,  -2.55555556,  -2.35353535,\n",
       "        -2.15151515,  -1.94949495,  -1.74747475,  -1.54545455,\n",
       "        -1.34343434,  -1.14141414,  -0.93939394,  -0.73737374,\n",
       "        -0.53535354,  -0.33333333,  -0.13131313,   0.07070707,\n",
       "         0.27272727,   0.47474747,   0.67676768,   0.87878788,\n",
       "         1.08080808,   1.28282828,   1.48484848,   1.68686869,\n",
       "         1.88888889,   2.09090909,   2.29292929,   2.49494949,\n",
       "         2.6969697 ,   2.8989899 ,   3.1010101 ,   3.3030303 ,\n",
       "         3.50505051,   3.70707071,   3.90909091,   4.11111111,\n",
       "         4.31313131,   4.51515152,   4.71717172,   4.91919192,\n",
       "         5.12121212,   5.32323232,   5.52525253,   5.72727273,\n",
       "         5.92929293,   6.13131313,   6.33333333,   6.53535354,\n",
       "         6.73737374,   6.93939394,   7.14141414,   7.34343434,\n",
       "         7.54545455,   7.74747475,   7.94949495,   8.15151515,\n",
       "         8.35353535,   8.55555556,   8.75757576,   8.95959596,\n",
       "         9.16161616,   9.36363636,   9.56565657,   9.76767677,\n",
       "         9.96969697,  10.17171717,  10.37373737,  10.57575758,\n",
       "        10.77777778,  10.97979798,  11.18181818,  11.38383838,\n",
       "        11.58585859,  11.78787879,  11.98989899,  12.19191919,\n",
       "        12.39393939,  12.5959596 ,  12.7979798 ,  13.        ])"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([-5.        , -4.8989899 , -4.7979798 , -4.6969697 , -4.5959596 ,\n",
       "       -4.49494949, -4.39393939, -4.29292929, -4.19191919, -4.09090909,\n",
       "       -3.98989899, -3.88888889, -3.78787879, -3.68686869, -3.58585859,\n",
       "       -3.48484848, -3.38383838, -3.28282828, -3.18181818, -3.08080808,\n",
       "       -2.97979798, -2.87878788, -2.77777778, -2.67676768, -2.57575758,\n",
       "       -2.47474747, -2.37373737, -2.27272727, -2.17171717, -2.07070707,\n",
       "       -1.96969697, -1.86868687, -1.76767677, -1.66666667, -1.56565657,\n",
       "       -1.46464646, -1.36363636, -1.26262626, -1.16161616, -1.06060606,\n",
       "       -0.95959596, -0.85858586, -0.75757576, -0.65656566, -0.55555556,\n",
       "       -0.45454545, -0.35353535, -0.25252525, -0.15151515, -0.05050505,\n",
       "        0.05050505,  0.15151515,  0.25252525,  0.35353535,  0.45454545,\n",
       "        0.55555556,  0.65656566,  0.75757576,  0.85858586,  0.95959596,\n",
       "        1.06060606,  1.16161616,  1.26262626,  1.36363636,  1.46464646,\n",
       "        1.56565657,  1.66666667,  1.76767677,  1.86868687,  1.96969697,\n",
       "        2.07070707,  2.17171717,  2.27272727,  2.37373737,  2.47474747,\n",
       "        2.57575758,  2.67676768,  2.77777778,  2.87878788,  2.97979798,\n",
       "        3.08080808,  3.18181818,  3.28282828,  3.38383838,  3.48484848,\n",
       "        3.58585859,  3.68686869,  3.78787879,  3.88888889,  3.98989899,\n",
       "        4.09090909,  4.19191919,  4.29292929,  4.39393939,  4.49494949,\n",
       "        4.5959596 ,  4.6969697 ,  4.7979798 ,  4.8989899 ,  5.        ])"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.0"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "loss_function(2,2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.0"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "loss_function(2,3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "w_try, b_try = np.meshgrid(np.linspace(-5,5), np.linspace(-5,5))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-5.        , -4.79591837, -4.59183673, ...,  4.59183673,\n",
       "         4.79591837,  5.        ],\n",
       "       [-5.        , -4.79591837, -4.59183673, ...,  4.59183673,\n",
       "         4.79591837,  5.        ],\n",
       "       [-5.        , -4.79591837, -4.59183673, ...,  4.59183673,\n",
       "         4.79591837,  5.        ],\n",
       "       ..., \n",
       "       [-5.        , -4.79591837, -4.59183673, ...,  4.59183673,\n",
       "         4.79591837,  5.        ],\n",
       "       [-5.        , -4.79591837, -4.59183673, ...,  4.59183673,\n",
       "         4.79591837,  5.        ],\n",
       "       [-5.        , -4.79591837, -4.59183673, ...,  4.59183673,\n",
       "         4.79591837,  5.        ]])"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "w_try"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-5.        , -5.        , -5.        , ..., -5.        ,\n",
       "        -5.        , -5.        ],\n",
       "       [-4.79591837, -4.79591837, -4.79591837, ..., -4.79591837,\n",
       "        -4.79591837, -4.79591837],\n",
       "       [-4.59183673, -4.59183673, -4.59183673, ..., -4.59183673,\n",
       "        -4.59183673, -4.59183673],\n",
       "       ..., \n",
       "       [ 4.59183673,  4.59183673,  4.59183673, ...,  4.59183673,\n",
       "         4.59183673,  4.59183673],\n",
       "       [ 4.79591837,  4.79591837,  4.79591837, ...,  4.79591837,\n",
       "         4.79591837,  4.79591837],\n",
       "       [ 5.        ,  5.        ,  5.        , ...,  5.        ,\n",
       "         5.        ,  5.        ]])"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "b_try"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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3XdF8rr8ilDEUIS+/In5e0ixnWf0EHoNZCCSPJ3k5yc+T/BzJ5w0lixBCiGEthAMAXmBm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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fba08f47e10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "w_try = np.linspace(-4,4,num=100)\n",
    "b_try = np.linspace(-4,4,num=100)\n",
    "\n",
    "w_try, b_try = np.meshgrid(w_try,b_try)\n",
    "e_obs = np.zeros((100,100))\n",
    "\n",
    "for i in range(100):\n",
    "    for j in range(100):\n",
    "        e_obs[i,j] = loss_function(w_try[i,j],b_try[i,j])\n",
    "        \n",
    "plt.pcolormesh(w_try, b_try, np.log(e_obs)) #using log here helps visualise better, you may take it out\n",
    "plt.xlabel('w')\n",
    "plt.ylabel('b')\n",
    "plt.title('Mean Squared Error')\n",
    "plt.show()        "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can see that the error in the plot is minimised at $w=2$ and $b=3$."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "355.06060606060601"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "e_obs.max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.0043879944910451526"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "e_obs.min()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Algorithms try to be a bit smarter by following the gradients to find the minimum. Remember we can only influence the error via the weights (and bias) `w,b`.\n",
    "\n",
    "## Maths (optional - no really)\n",
    "\\begin{align}\n",
    "\\mathcal{L} & = \\sum_{i=1}^N (y_i-f(x_i,w,b))^2 \\\\\n",
    "\\frac{\\delta\\mathcal{L}}{\\delta w} & = \\sum_{i=1}^N 2(y_i-f(x_i,w,b))\\frac{\\delta f(x_i,w,b)}{\\delta w} \\\\ \n",
    "& = \\sum_{i=1}^N 2\\xi_i\\frac{\\delta f(x_i,w,b)}{\\delta w}\n",
    "\\end{align}\n",
    "where $\\xi_i$ is the error term $y_i-f(x_i,w,b)$ and \n",
    "$$\n",
    "\\frac{\\delta f(x_i,w,b)}{\\delta w} = x_i\n",
    "$$\n",
    "\n",
    "Similar expression can be found for $\\frac{\\delta\\mathcal{L}}{\\delta b}$ (exercise).\n",
    "\n",
    "Finally the weights can be updated as $w_{new} = w_{current} - \\gamma \\frac{\\delta\\mathcal{L}}{\\delta w}$ where $\\gamma$ is a learning rate between 0 and 1."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LinearRegression"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Scikit learn is another Machine Learning that is quite powerful on itself. However it may not scale to millions of data points same way that DL does.\n",
    "\n",
    "## Noisy Data\n",
    "We see how to find the weights using scikit learn below. However, the data isn't as perfect as before. We add a tiny bit of noise."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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druvHmLYzPmUYw07qeFdreuM+27/Sm9I6VpYxLfbz2LuVaB/Vd0dEw/bPSbrf9jOt/1HL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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fb9fe0a2198>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "x = np.random.randn(10,1)\n",
    "y = 2*x+3 + 0.1*np.random.randn(10,1)\n",
    "\n",
    "plt.scatter(x,y)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-0.0710438 ],\n",
       "       [-1.63204099],\n",
       "       [ 0.61393732],\n",
       "       [-0.72602171],\n",
       "       [ 0.56296216],\n",
       "       [-1.19009865],\n",
       "       [ 0.21120869],\n",
       "       [ 1.11886921],\n",
       "       [-0.21082568],\n",
       "       [-2.44257088]])"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression(copy_X=True, fit_intercept=True, n_jobs=1, normalize=False)"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model = LinearRegression() # \n",
    "model.fit(x,y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 2.00976455]])"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.coef_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 3.03645155])"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.intercept_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 43.2317426]])"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.predict(20)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Note:\n",
    "1. `model.fit(...)` and `model.predict` will be two of the most important functions you will use all throughout. Worth remembering!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "43"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "2*20+3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fb9fd900ba8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "x_test = np.linspace(-3,3)\n",
    "y_pred = model.predict(x_test[:,None])\n",
    "\n",
    "plt.scatter(x,y)\n",
    "plt.plot(x_test,y_pred,'r')\n",
    "plt.legend(['Predicted line','Observed data'])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## A more complicated function\n",
    "Remember that when we say linear regression it mean linear in the parmaters $w, b$ not $x$! See illustration below:\n",
    "\n",
    "\\begin{align}\n",
    "y = b +w^T X \\\\\n",
    "X = [x\\quad x^2\\quad x^3] \\\\\n",
    "y = b +wx +w_2 x^2 +w_3 x^3 \\\\\n",
    "\\end{align}\n",
    "The shapes of the matrices are:\n",
    "- X = NxD matrix\n",
    "- y = Nx1 vector\n",
    "- w = Dx1 vector\n",
    "\n",
    "Note in the example below that there is no $x^2$ term. i.e. it's coefficient is zero!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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YnlvK2f2jTul7fHwMP+obSaDfsWejFJH2SS18D1BUXs3v3k2htKqW3lEdmDYk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\ni4h4if8HltgRSiWyMt8AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fb9fd87e278>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "x = np.linspace(-5,5,num=100)[:,None]\n",
    "y = -0.5 + 2.2*x +0.3*x**3+ 2*np.random.randn(100,1)\n",
    "\n",
    "plt.plot(x,y)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(100, 1)"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(100, 4)"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_new = np.hstack([x,x**2,x**3,x**4])\n",
    "x_new.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(100, 1)"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression(copy_X=True, fit_intercept=True, n_jobs=1, normalize=False)"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model = LinearRegression()\n",
    "model.fit(x_new,y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[  2.22229669e+00,  -1.39017445e-02,   2.96369412e-01,\n",
       "          2.16217922e-03]])"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.coef_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Note:\n",
    "- The coefficient of $x^2$ and $x^4$ is really close to zero."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([-0.65870273])"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.intercept_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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AagijOStoKt68ToNL8fhn/dfgUjwNLsdT/3ICZdNu3TnQy4srdfzZVbYOBzq0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\nH0lUGwCyn/YGAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fb9fd8ec588>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "y_pred = model.predict(x_new)\n",
    "\n",
    "plt.scatter(x,y)\n",
    "plt.plot(x_new[:,0],y_pred,'r')\n",
    "plt.legend(['Predicted line','Observed data'])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[  -5.        ,   25.        , -125.        ,  625.        ],\n",
       "       [  -4.8989899 ,   24.00010203, -117.57625742,  576.00489747]])"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.preprocessing import PolynomialFeatures\n",
    "poly = PolynomialFeatures(degree=4,include_bias=False)\n",
    "x_new2 = poly.fit_transform(x)\n",
    "x_new2[:2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(100, 4)"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_new.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[  -5.        ,   25.        , -125.        ,  625.        ],\n",
       "       [  -4.8989899 ,   24.00010203, -117.57625742,  576.00489747]])"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_new[:2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.1"
  },
  "latex_envs": {
   "bibliofile": "biblio.bib",
   "cite_by": "apalike",
   "current_citInitial": 1,
   "eqLabelWithNumbers": true,
   "eqNumInitial": 0
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}